Improving contrast between gray and white matter of Logan graphical analysis’ parametric images in positron emission tomography through least-squares cubic regression and principal component analysis

2021 ◽  
Vol 7 (3) ◽  
pp. 035003
Author(s):  
Paulus Kapundja Shigwedha ◽  
Takahiro Yamada ◽  
Kohei Hanaoka ◽  
Kazunari Ishii ◽  
Yuichi Kimura ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component Analysis ◽  
White Matter ◽  
Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Graphical Analysis ◽  
Emission Tomography ◽  
Parametric Images ◽  
Positron Emission

scholarly journals Masked volume wise principal component analysis of small adrenocortical tumours in dynamic [11C]-metomidate positron emission tomography

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Pasha Razifar ◽  
Joakim Hennings ◽  
Azita Monazzam ◽  
Per Hellman ◽  
Bengt Långström ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Tomography ◽  
Positron Emission ◽  
Adrenocortical Tumours

Principal component analysis of dynamic positron emission tomography images

1994 ◽  
Vol 21 (12) ◽  
pp. 1285-1292 ◽  
Author(s):  
F. Pedersen ◽  
M. Bergströme ◽  
E. Bengtsson ◽  
B. Långström
Keyword(s):  
Positron Emission Tomography ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Tomography ◽  
Dynamic Positron Emission Tomography ◽  
Positron Emission

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

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